VC++小波变换技术与工程实践源代码

小波理论与应用书中参考程序，二进小波变换图像增强及数字水印。c++程序-Wavelet Theory and Applications book reference procedure, the dyadic wavelet transform image enhancement and digital watermarking. c++ program

给予小波变换的图像处理，能够形成图像的分割与拼接-Given wavelet transform image processing, image segmentation can be formed with the splicing

是二维图象的小波变换和逆变化.可以直接编译运行和调试-is two-dimensional image inverse wavelet transform and change. Can be directly translated running and debugging

Daubechies 小波-Daubechies wavelet

基于混沌加密的离散小波变换域数字图像水印算法的研究的期刊论文-DWT Based on Chaotic Encryption Algorithm for Digital Image Watermarking domain of research journal articles

这是小波变换软件在图像处理中的应用，该软件是基于MFC的，可以处理不同大小的图像，同样也可以更换小波函数，实现不同的小波变换。-This is the wavelet transform the image processing software, the application of the software is based on the MFC, can handle different sizes of the images, but also can be replaced wavelet function, allowing different wavelet transform.

vc写的实现小波处理，滤波，复原，融合等源程序-vc wrote realize wavelet processing, filtering, restoration, integration, such as source

二维小波分析对图像处理的应用-wavelet analysis of 2D image processing applications

二进小波，非极大值抑制，模极大值的边缘检测程序-Dyadic wavelet, non-maxima suppression, modulus maxima of the edge detection procedure

关于二维小波变换的程序 [精华] 说明：此算法重在概念，速度并不是很快。因为FOR循环的缘故。此程序从循环矩阵的观点出发，把圆周卷积和快速幅里叶变换建立了联系。实现了分解和无失真重构。它只做了一层分解，即将256x256图形分解成为64x64的四个图形，避免了使用WKEEP()的困惑。主要思想为用小波滤波器族构造正交阵W,变换写为B=W*A*W ,反变换为：A=W *A*W,这与所有正交变换无异。W为循环正交矩阵，因此可用FFT实现快速运算，难点就在重构矩阵上。若用矩阵概念明确，一个共扼转置可以搞顶。但FFT的使用必须找到与分解序列的关系。-on Wavelet Transform procedures [best] Note : This algorithm important concept is not speed quickly. Because FOR cycle nowadays. This program cycle from the point of view of Matrix, circular convolution and fast Fourier transform is established links. To achieve the decomposition and reconstruction without distortion. It only had one decomposition, 256x256 graphics will be 64x64 decomposition of the four graphics, avoiding the use of WKEEP () confusion. The main idea of using wavelet filter generator orthogonal array W, write to transform B = W* A* W, anti-Transform : A = W* A* W, which is orthogonal transformation all the same. W cycle orthogonal matrix, can be used to achieve rapid FFT computation, on the dif